arxiv: 2604.10700 · v1 · submitted 2026-04-12 · 📡 eess.IV

Recognition: unknown

VCC-DSA: A Novel Vascular Consistency Constrained DSA Imaging Model for Motion Artifact Suppression

Guanyu Yang, Hui Tang, Jian Lu, Jun Xiang, Peng Yuan, Rongjun Ge, Rong Yan, Shuo Li, Weilong Mao, Yang Chen, Yikun Zhang, Yudong Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:18 UTC · model grok-4.3

classification 📡 eess.IV

keywords digital subtraction angiographymotion artifact suppressionvascular consistencydeep learningimage enhancementDSA imaging

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The pith

VCC-DSA uses a vascular consistency strategy to suppress motion artifacts in digital subtraction angiography.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper proposes VCC-DSA, a novel imaging model designed to address motion artifacts in DSA caused by moving high-attenuation tissues. It features a learning-based subtraction mapping, residual dense blocks for complex structures, an innovative vascular consistency strategy to extract vessel details from relative motions, and a mixup-based data self-evolution for better training. The result is significantly improved image quality, with PSNR and SSIM gains of 73.4% and 8.56% over other methods, validated on clinical human data and animal experiments.

Core claim

The core discovery is that enforcing vascular consistency across mask and live images allows spontaneous distillation of the contrast-enhanced vessel structure, solving the ill-posed subtraction problem and robustly suppressing artifacts even in overlapping or small vessel cases.

What carries the argument

Vascular Consistency Strategy that extracts intrinsic consistency from various relative motions in mask-live images to distill the vascular structure with contrast-agent development.

If this is right

Enhances stability for cases where moving bones overlap with blood vessels.
Improves visibility of small peripheral vessels.
Reduces the need for highly matched training data pairs.
Promotes better learning of vascular features while excluding irrelevant artifacts in labels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This method might extend to other medical imaging modalities affected by motion, such as CT angiography.
Could allow for DSA procedures with reduced patient sedation by handling more natural movements.
Supports development of automated real-time artifact correction in interventional radiology.

Load-bearing premise

Vascular structures maintain identifiable consistency in their appearance across different relative motions between mask and live images.

What would settle it

Demonstrating no significant improvement in PSNR or SSIM on an independent clinical DSA dataset with varied patient motions would challenge the model's effectiveness.

Figures

Figures reproduced from arXiv: 2604.10700 by Guanyu Yang, Hui Tang, Jian Lu, Jun Xiang, Peng Yuan, Rongjun Ge, Rong Yan, Shuo Li, Weilong Mao, Yang Chen, Yikun Zhang, Yudong Zhang.

**Figure 2.** Figure 2: Overall architecture of the proposed VCC-DSA imaging model. It benefits from four special designs: (a) Learning-based Subtraction Mapping [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: (a) The proposed RDB-based network. The network consists of two parts, namely, the feature extractor and the feature fusion. The feature [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: MDSS evolutionally optimizes training data for new live image [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Digital subtraction angiography results. Our method (d) has the characteristics of clear vascular structure, no motion artifacts, and clear [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 6.** Figure 6: The proposed VCC-DSA method achieves robust cross-dataset generalizability [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

**Figure 7.** Figure 7: The proposed method still demonstrates significant DSA imaging superiority with precise vascular imaging and robust artifact suppression, even [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Ablation performance of the Learning-based Subtraction Mapping Paradigm in qualitative analysis. Our proposed LSMP (d) with mask image (b) [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 9.** Figure 9: Ablation performance of Vascular Consistency Strategy in quan [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 10.** Figure 10: Ablation performance of Vascular Consistency Strategy in visual comparison experiments with [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Ablation performance of Mixup-based Data Self-evolution Strategy in qualitative experiments, and comparison with pixel shift. By comparing the [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗

**Figure 12.** Figure 12: The proposed method enables high-quality DSA imaging with precise vascular structure and robust artifact suppression for most common clinical [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗

**Figure 13.** Figure 13: As red arrow indicated in the enlarged region, partial [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗

**Figure 13.** Figure 13: Failed cases in experiments. number of experiments demonstrate the effectiveness and robustness of our model in DSA imaging. The image quality advantage of our method can reduce the artifacts caused by patient movement in clinical applications, thereby reducing the possibility of re-acquiring DSA data, saving valuable time for the treatment of cardiovascular diseases, especially acute stroke. Declarati… view at source ↗

read the original abstract

Digital Subtraction Angiography (DSA) is a clinically significant imaging technique for diagnosing cerebrovascular disease, as gold-standard. However, the artifacts caused by motion of high-attenuation tissues such as bones, teeth, and catheters, seriously reduce the visibility of blood vessels. This paper presents a novel Vascular Consistency Constrained DSA Imaging Model (VCC-DSA) for robust motion suppression and precise vascular imaging with the following designs: 1) We specially design a Learning-based Subtraction Mapping Paradigm, so that the ill-posed problem of existing learning-based methods can be solved to enhance the stability of the algorithm. 2) Our model effectively develops Residual Dense Blocks and details-shortcut to improve the performance under complex structures, such as moving bones overlapping with blood vessels, and small features, like peripheral vessels. 3) An innovative Vascular Consistency Strategy is proposed to extract intrinsically consistency from the various relative motions in mask-live images, so that spontaneously distils the vascular structure with contrast-agent development and robustly suppress motion artifacts, and also naturally alleviates the high matching requirements of data. 4) We creatively design a Mixup-based Data Self-evolution Strategy for data-intra self-enhancement in training loop, so that the training data gains dynamically optimized to promote model better learning the vascular features, and excluding the irrelevant structures in live/mask image and even the inevitable-artifacts/fake-structure in label. Prospectively, to further evaluate practical value, an actual general anesthesia animal experiment is specially conducted, besides the assessment on human clinical data. Compared with other method, our model improves the PSNR and SSIM by 73.4% and 8.56%, respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

VCC-DSA bundles a learned subtraction step, residual dense blocks, a vascular consistency idea, and mixup data evolution into one model for DSA motion artifacts, with claimed large gains and an animal test, but the consistency mechanism stays underspecified.

read the letter

The paper's core contribution is VCC-DSA, which tackles motion artifacts in digital subtraction angiography by learning a subtraction mapping instead of relying on perfect mask-live pairs, adding residual dense blocks with detail shortcuts for complex overlaps and small vessels, introducing a vascular consistency strategy to pull out contrast-filled structures from relative motions, and using mixup-based data self-evolution to refine training samples on the fly. They also ran a general-anesthesia animal experiment alongside clinical data to check real-world behavior. These pieces address a genuine clinical pain point where bone and catheter movement hides vessels during cerebrovascular procedures. The data self-evolution step is a practical way to deal with inevitable label artifacts, and the animal setup adds some grounding beyond synthetic tests. The reported 73.4% PSNR and 8.56% SSIM lifts over prior methods are the headline numbers, but they sit on top of the full model without clear breakdowns. The vascular consistency strategy is described as spontaneously distilling vascular structure from various motions and reducing the need for exact matching, yet the abstract gives no loss term, correlation measure, or attention mechanism to show how that happens. Without ablations isolating its effect or equations formalizing the consistency constraint, it's difficult to know whether the gains come mainly from the dense blocks and subtraction paradigm or from this new piece. Experimental setup details on baselines, statistical tests, and potential biases in the animal and clinical sets are also missing from the summary. This work is aimed at medical imaging researchers focused on interventional radiology and artifact reduction. Readers already building subtraction or consistency models could extract the architecture choices and data-handling trick for their own pipelines. It shows enough concrete engineering and a real-data test to merit serious referee time rather than a desk reject, though the review will need to press for the missing formalization and component tests.

Referee Report

3 major / 2 minor

Summary. The paper proposes VCC-DSA, a deep learning model for motion artifact suppression in Digital Subtraction Angiography (DSA) imaging. It introduces four designs: (1) a learning-based subtraction mapping paradigm to stabilize the ill-posed subtraction problem, (2) residual dense blocks with details-shortcut for handling complex overlapping structures and small vessels, (3) a Vascular Consistency Strategy to extract intrinsic consistency from relative motions in mask-live pairs and spontaneously distill vascular contrast, and (4) a Mixup-based data self-evolution strategy for dynamic training data optimization. The model is tested on human clinical data and a prospective animal experiment under general anesthesia, with the central claim being 73.4% PSNR and 8.56% SSIM improvement over prior methods.

Significance. If the performance gains are reproducible and attributable to the proposed components, the work addresses a clinically important problem in cerebrovascular DSA by reducing motion artifacts from bones, teeth, and catheters. The Mixup self-evolution and consistency constraint could offer generalizable training strategies for medical image enhancement tasks with imperfect data pairing. The prospective animal experiment under anesthesia is a clear strength, adding translational relevance beyond retrospective clinical datasets.

major comments (3)

[Abstract and §3] Abstract and §3 (Vascular Consistency Strategy): The strategy is claimed to 'spontaneously distil the vascular structure with contrast-agent development' from relative motions in mask-live images and to alleviate high matching requirements, but no equation, loss term, consistency metric (e.g., temporal correlation or attention), or ablation isolating its contribution from the residual dense blocks or subtraction mapping is provided. This is load-bearing for the robustness claim.
[Experimental results section] Experimental results section (quantitative comparison): The headline 73.4% PSNR and 8.56% SSIM gains are presented without naming the exact baseline methods, test-set size, standard deviations, or statistical significance tests. This prevents assessment of whether the gains derive from the full VCC-DSA or from training choices, directly undermining the superiority assertion.
[Animal experiment section] Animal experiment section: The prospective general-anesthesia animal study is noted for practical value, yet no quantitative PSNR/SSIM results, motion-induction protocol, or direct comparison to the clinical-data metrics are reported. This leaves the translational claim unsupported by evidence.

minor comments (2)

[Abstract] Abstract: 'Compared with other method' should explicitly list the compared algorithms and cite their references for reproducibility.
[Throughout] Notation: Ensure PSNR, SSIM, and DSA are defined at first use; clarify whether the reported percentages are relative improvements or absolute differences.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's report. We value the constructive criticism and have prepared detailed responses to each major comment. Revisions will be made to address the identified gaps in clarity and evidence.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Vascular Consistency Strategy): The strategy is claimed to 'spontaneously distil the vascular structure with contrast-agent development' from relative motions in mask-live images and to alleviate high matching requirements, but no equation, loss term, consistency metric (e.g., temporal correlation or attention), or ablation isolating its contribution from the residual dense blocks or subtraction mapping is provided. This is load-bearing for the robustness claim.

Authors: We agree that additional formalization is necessary to fully substantiate the Vascular Consistency Strategy. In the revised manuscript, we will introduce the mathematical formulation, including the specific consistency loss term and metric (such as temporal correlation across mask-live pairs). Furthermore, we will conduct and report an ablation study to isolate the contribution of this strategy, demonstrating its role in distilling vascular structures and reducing matching requirements beyond the effects of residual dense blocks and the subtraction mapping paradigm. revision: yes
Referee: [Experimental results section] Experimental results section (quantitative comparison): The headline 73.4% PSNR and 8.56% SSIM gains are presented without naming the exact baseline methods, test-set size, standard deviations, or statistical significance tests. This prevents assessment of whether the gains derive from the full VCC-DSA or from training choices, directly undermining the superiority assertion.

Authors: We apologize for the omission of these details in the experimental results section. The revised version will explicitly name all baseline methods compared, specify the test-set size (including number of images and patients), report standard deviations for the metrics, and include statistical significance tests (e.g., Wilcoxon signed-rank tests) to validate the improvements. This will allow readers to better evaluate the source of the performance gains. revision: yes
Referee: [Animal experiment section] Animal experiment section: The prospective general-anesthesia animal study is noted for practical value, yet no quantitative PSNR/SSIM results, motion-induction protocol, or direct comparison to the clinical-data metrics are reported. This leaves the translational claim unsupported by evidence.

Authors: We acknowledge that the animal experiment section lacks quantitative metrics and a detailed protocol description. In the revision, we will provide a full description of the motion-induction protocol used in the prospective study. Regarding quantitative PSNR/SSIM, since the animal data was collected under controlled anesthesia to demonstrate real-world applicability, we did not compute the same metrics as on the clinical dataset; however, we will add any available quantitative assessments or clarify the qualitative evaluation approach and its relation to clinical results to better support the translational relevance. revision: partial

Circularity Check

0 steps flagged

No circularity in VCC-DSA derivation chain; designs and results are independent

full rationale

The abstract and provided description introduce four distinct architectural and training designs (Learning-based Subtraction Mapping Paradigm, Residual Dense Blocks with details-shortcut, Vascular Consistency Strategy, and Mixup-based Data Self-evolution Strategy) as separate contributions to address motion artifacts. No equations, loss terms, or derivations are shown that reduce any claimed output (e.g., vascular structure distillation or PSNR/SSIM gains) to self-definition, fitted inputs relabeled as predictions, or self-citation chains. The quantitative improvements are presented as empirical comparisons against other methods on external clinical and animal data, with no load-bearing uniqueness theorems or ansatzes imported from prior author work. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

The central claim depends on the effectiveness of these new strategies and the assumption that the learning paradigm solves the ill-posed problem, with no independent verification provided in the abstract.

free parameters (1)

Model hyperparameters and network weights
Deep neural network parameters are fitted to the training data to learn the subtraction mapping and consistency features.

axioms (1)

domain assumption There exists intrinsic vascular consistency across mask and live DSA images despite relative motions
This is the basis for the Vascular Consistency Strategy to distil vascular structure.

invented entities (2)

Vascular Consistency Strategy no independent evidence
purpose: To extract consistency from relative motions in mask-live images for artifact suppression
Newly proposed component in the model.
Mixup-based Data Self-evolution Strategy no independent evidence
purpose: For dynamic optimization of training data to better learn vascular features
Newly proposed for self-enhancement in training loop.

pith-pipeline@v0.9.0 · 5643 in / 1512 out tokens · 76857 ms · 2026-05-10T15:18:57.537215+00:00 · methodology

discussion (0)

Reference graph

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